Current status of AMSR-E data utilization in JMA/NWP

Masahiro KAZUMORI

Numerical Prediction DivisionJapan Meteorological Agency

14-16 July 2008 Joint AMSR Science Team Meeting, Telluride, CO

14-16 July 2008 Joint AMSR Science Team Meeting, Telluride, CO 2

Contents• Utilizations of AMSR-E data in JMA/NWP

– Assimilation• Radiance assimilation for Global model (GSM)• Retrieval (TPW, Rain rate) assimilations for Mesoscale Model (MSM)

– Verification of the forecast models• Total Precipitable Water• Monthly Rainfall• Heavy rain and typhoon case study

• Case study : Cyclone Nargis in Myanmar 2008– Impact of MW-Imager data in JMA operational system– Use of all weather wind speed from AMSR-E (on going developmen

t)

14-16 July 2008 Joint AMSR Science Team Meeting, Telluride, CO 3

JMA/NWP models

Model

Global Spectral

Model (GSM)

Meso Scale Model (MSM)

Horizontal res. 20km 5km

Vertical res. (model top)

60 (0.1hPa) 50 (21.8km)

Forecast range(Initial time)

84h (00,06,18UTC)216h (12UTC)

15h (00,06,12,18UTC)33h (03,09,15,21UTC)

frequency 4/day 8/day

TargetOne-week forecast

Short-range forecastAeronautical forecast

Disaster prevention information

Data Assimilation 4D-Var 4D-Var

20kmGSM

Observation

14-16 July 2008 Joint AMSR Science Team Meeting, Telluride, CO 4

Utilization of MW Imager data in GSM Assimilation of Microwave Radiometer (MWR) radiances from DMSP/SSMI, TRMM/T

MI and Aqua/AMSR-ELess cloud-affected radiances over the ocean with SST > 5 deg.COnly vertical polarized channels at 19 – 89 GHz( to obtain moisture information)VarBC corrects biases against analysis

Impacts on analyses/forecastsBetter TPW (Total Precipitable Water) analysis verified against TRMM retrieved TPWBetter precipitation forecasts: larger correlation between 1-day-forecast and GPC

P (0.881=>0.891 for Aug2004)Better typhoon track forecasts

5 May

-2

-1

0

1

2

3

4

30A

pr

05M

ay

10M

ay

15M

ay

20M

ay

25M

ay

30M

ay

BIASRMSE

Start MWR radiance assimilation

Global AnalysisGlobal Analysis

TRMMTRMM

differencedifference

no M

WR

rad

ian

ce

no M

WR

rad

ian

ce

Global AnalysisGlobal Analysis

TRMMTRMM

differencedifference

use M

WR

rad

ian

ce

use M

WR

rad

ian

ce

25May

Time sequence of TPW RMSE and bias between analysis and TRMM retrieval

Time sequence of TPW RMSE and bias between analysis and TRMM retrieval

14-16 July 2008 Joint AMSR Science Team Meeting, Telluride, CO 5

Utilization of MW Imager data in MSM

Retrieval assimilation (Total Precipitable Water and Rain Rate from AMSR-E, TMI and SSMI)

Impacts : Better rainfall forecasts

RR [mm/3hr]

TCPW [mm]

RR [mm/3hr]

TCPW [mm]

Conventional Data

MWRs RR

RA Obs.

MWRs TCPW

MWRs RR

RA Obs.

RA + MW TCPW & RR

A case study : Fukui Heavy Rain in 2004“Assimilation of the Aqua/AMSR-E data to Numerical Weather Predictions”, Tauchi et al., IGARSS04 Poster

14-16 July 2008 Joint AMSR Science Team Meeting, Telluride, CO 6

Verification : Total Precipitable WaterInitial TPW 3-day

Forecast Observation (AMSR-E)

Initial -Obs Forecast - Obs Monthly average for Aug. 2007

GSM : Dry bias in deep convective area and wet bias along the ITCZ.

The biases increase with forecast time.

Satellite measurements reveal the numerical model biases.

14-16 July 2008 Joint AMSR Science Team Meeting, Telluride, CO 7

Verification : Rainfall forecast

Rainfall forecast in JMA Global Model is excessive, especially for deep convective area in the early stage of forecast.

Satellite measurements are essential to evaluate the performance of JMA Global model and provide important information for further forecast model improvement.

AMSR-E RAINGSM FT=24

GSM FT=72

Monthly Averaged R24 for Aug. 2007

14-16 July 2008 Joint AMSR Science Team Meeting, Telluride, CO 8

Verification : Heavy rain and typhoon

FT=14 Jul. 12 03UTC init.

MSM Rain forecastValid time : 17 UTC July 12, 2007

FT=08 Jul. 12 09UTC init.

FT=02 Jul. 12 15UTC init.

Rain from AMSR-E Measurement

Fake rains predicted in subsidence area of subtropics high were identified by comparison with AMSR-E measurement and indicate issues on current cumulus parameterization scheme in MSM.

Fake rain?MTSAT IR Image

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Case study: Cyclone Nargis in May 2008

Forecast comparisons for several NWP centers (12UTC April 30, 2008 Initial forecast, Psea [hPa])

Observed cyclone track

Cyclone Nargis : Strong tropical cyclone made landfall in Myanmar on May 2, 2008

JMA ECMWF

NCEP

Day-2

Day-1

Most of NWP centers predicted the cyclone landfall in Myanmar. But, the intensity of JMA forecast is weak.

14-16 July 2008 Joint AMSR Science Team Meeting, Telluride, CO 10

JMA GSM Forecast (Rain and TPW)

Psea and Rain24 TPW

In the Bengal bay, foregoing moisture flow from south west was well analyzed.

The direction forecast of the cyclone was “eastward”.

The JMA global forecast predicted the May 2 landfall.

JMA GSM 12UTC April 30 Init.

Day-1

Forecast

Day-2

Forecast

JMA/GSM

Analysis

Psea [hPa] and Rain [mm/day]

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Impact of MW-Imager data for TPWInitial Difference (W/ – W/O)

Day-1 forecast diff (W/ – W/O)

Data Coverage (MW-Imager) O-B [K] Case Study:

W/: Same data usage as operational

W/O : Without all MW Imager data in the analysis

[mm] [mm]

MW-Imager data (SSMI radiance) enhanced the moisture flow from south west and the impacts was retained for 24-hour TPW forecast. However, rain affected data were not used.

Rain affected data

14-16 July 2008 Joint AMSR Science Team Meeting, Telluride, CO 12

Use of all weather wind speed from AMSR-E

• JAXA’s research products developed by Dr. Shibata and Mr. Saitoh.• Ocean surface wind speed retrievals under all weather condition usin

g AMSR-E low frequency channels.

• JMA developed a QC scheme for the data assimilation in JMA global 4D-Var system and started to study the impact on analysis and forecast.

• Developed QC in JMA– Data selection (Above 7m/s)– Removal of land (and/or island) contaminated data.– Removal of sea ice contaminated data.– Thinning and averaging with 100km grid boxes.– Gross error check based on wind speed O-B.

14-16 July 2008 Joint AMSR Science Team Meeting, Telluride, CO 13

AMSR-E all weather wind speed dataJAXA L2 wind

speedAll weather wind data

Averaging with 100km grid boxes

Strong winds in the cyclone core are available.

Need to make average with grid boxes to reduce noise and fit to model resolution (4D-Var :Inner model res. 80km).

06UTC 29 April, 2008

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The first analysis increment

AMSR-E all weather wind speed data strengthen the intensity and the surface wind speed in the analysis.

Used data Psea (W AMSR-E) Psea (W/O AMSR-E)

Increment of wind

14-16 July 2008 Joint AMSR Science Team Meeting, Telluride, CO 15

Impacts on forecastsForecast comparisons for several NWP centers (1

2UTC April 30, 2008 Initial forecast, Psea [hPa])

JMA ECMWF

NCEP

Day-2

Day-1

JMA + AMSR-E

The intensity in forecast was also strengthened.

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Impacts on forecasts

Cyclone Nargis Forecast and Analysis

No significant improvement in the track forecast

Central Pressure

Max wind

AMSR-E all weather wind speed data strengthen the intensity and wind.

Red: W AMSR-E, Green: W/O AMSR-E

16 cases for each

14-16 July 2008 Joint AMSR Science Team Meeting, Telluride, CO 17

Conclusions• AMSR-E data has been utilizing in JMA operational NWP Assimilation

– Clear radiance assimilation for GSM– Retrieval (TPW and Rain rate) assimilations for MSM AMSR-E provide much information on rain and moisture for NWP

Model Verifications– Total Precipitable Water– Rain distribution– Heavy rain and typhoon study MW imager measurements (Rain and TPW) reveal the forecast model errors (bias and accuracy)

and the results lead to further forecast model improvement (Cumulus parameterization scheme)

• Case study : Cyclone Nargis in Myanmar 2008 MW-Imager data play important roles to provide moisture information in GSM Difficulty of cloud and rain affected radiance assimilation All weather wind speed data were investigated in JMA/NWP

– Available under severe weather condition (e.g. Tropical cyclone)– Assimilation experiments

• Strengthen the intensity and the max wind speed• No significant improvement in the track forecast

– Valuable data to provide realistic observational information under severe weather condition